# Entity Resolution: Connecting Fragmented Business Records

January 29, 2026

Learn how entity resolution matches and links business records across data sources to create unified, verified business identities.

**Entity resolution** is the process of matching, linking, and deduplicating records that refer to the same real-world business across different data sources. It’s the technical foundation that transforms fragmented business data into verified identities.

## The Problem Entity Resolution Solves

Business information is scattered across thousands of sources—state registries, credit bureaus, web data, transaction records, and more. The same business appears differently in each:

- **State filing**: “GTL Services LLC”
- **Website**: “Green Thumb Landscaping”
- **Credit card processor**: “GREEN THUMB LANDSCAPE”
- **Google listing**: “Green Thumb Landscaping & Lawn Care”

Without entity resolution, these look like four different businesses. With it, they’re recognized as one.

## Why Entity Resolution Is Hard

### Name Variation

Businesses operate under multiple names:

- Legal names vs. trade names (DBAs)
- Abbreviations and acronyms
- Spelling variations and typos
- Name changes over time

### Address Complexity

- Registered addresses differ from operating locations
- Suite numbers, formatting inconsistencies
- Businesses relocate
- Multiple locations under one entity

### Ownership Structures

- Parent companies and subsidiaries
- Franchises with shared branding but separate entities
- Complex corporate hierarchies

### Data Quality

- Outdated records
- Incomplete information
- Conflicting data across sources
- Intentional obfuscation

## Entity Resolution Techniques

### Deterministic Matching

Exact matches on unique identifiers:

- EIN (Employer Identification Number)
- State registration numbers
- DUNS numbers

**Pros**: High precision, fast
**Cons**: Requires exact match; many records lack identifiers

### Probabilistic Matching

Statistical comparison of multiple attributes:

- Name similarity algorithms (edit distance, phonetic matching)
- Address standardization and comparison
- Weighted scoring across attributes

**Pros**: Handles variation and partial matches
**Cons**: Requires tuning; can produce false positives

### Graph-Based Resolution

Connecting records through relationships:

- Shared addresses link entities
- Common officers or owners
- Business relationships and transactions

**Pros**: Captures complex structures
**Cons**: Computationally intensive; requires relationship data

## Entity Resolution in KYB

Entity resolution is essential for effective [KYB](/content/resources/knowledge/what-is-kyb/index.html):

**Verification accuracy**: Correctly matching a business application to its official registration—even when names differ—enables accurate verification.

**Ownership tracing**: Linking entities through ownership chains to identify [ultimate beneficial owners](/content/resources/knowledge/glossary-ubo/index.html).

**Risk detection**: Recognizing when multiple applications share suspicious patterns (same registered agent, same formation date, same address).

**Deduplication**: Ensuring the same business isn’t onboarded multiple times under different names.

## The Quality Spectrum

Entity resolution exists on a spectrum from basic to comprehensive:

| Level       | Approach                             | Result                                   |
|-------------|--------------------------------------|------------------------------------------|
| **None**    | Exact name match only                | Misses most legitimate matches           |
| **Basic**   | Simple fuzzy matching                | High false positive rate                 |
| **Intermediate** | Multi-attribute probabilistic   | Reasonable accuracy                      |
| **Advanced** | Graph-based with multiple sources    | High accuracy, reveals structure         |

The right level depends on risk tolerance and use case. High-stakes decisions (lending, compliance) demand advanced resolution.

## Key Takeaways

- **Entity resolution connects fragmented records** into unified business identities
- **Name variation is the core challenge**—businesses appear differently across sources
- **Multiple techniques exist**—deterministic, probabilistic, and graph-based
- **Resolution quality directly impacts KYB accuracy**—poor resolution means missed matches or false positives
- **Advanced resolution reveals structure**—ownership chains, related entities, and risk patterns